Directory assistance has been necessary since the onset of the public switched telephone system. In the beginning, operators would manually find listings according to caller requests. As the telephone networks grew, increased demand for directory assistance followed, and automation became necessary to control the costs associated with providing listing information to callers. Modern directory assistance automation has continued to progress from this archaic system throughout the years.
A major development was the advent of special switches to connect directory assistance calls to dedicated operators. The switches are referred to as Toll Operator Position Switches (TOPS). During a directory request, callers are connected to an operator having access to a database of listings. The operator asks the caller for the requested listing and locality and queries the database for the appropriate number. Upon determining the number, the operator gives the caller the number. Current systems now automate the announcement of the number by having the TOPS connect the caller to an announcement system, which synthesizes or plays a voice announcement of the number.
The next significant advance automated the operator's request for the listing and locality from the caller by providing a synthesized or recorded listing and locality request. The caller's response is recorded and automatically played to the next available operator, who queries the database for the proper listing. This automation technique is referred to as “store and forward.” The store and forward technique is currently used in directory assistance automation.
The next phase of automation incorporated speech recognition into automated directory assistance systems to identify numbers for frequently requested listings (FRLs) in a given locality without any interaction from an operator. Unfortunately, these systems have not been commercially accepted for several reasons. The percentage of directory assistance calls that existing systems have been able to fully automate is low. Further, the cost and time required to develop and implement such systems is prohibitive given the low percentage of automation.
In operation, directory assistance systems facilitate access to one or more directory assistance databases containing listings for any number of localities. Historically, when an operator handles a call, the operator will develop the database query, initiate the database search, and parse through the returned listings to find the number requested. The operator will signal the TOPS to connect the caller to the announcement system, which will announce the requested number.
When the call is fully automated to the exclusion of the operator, current directory assistance systems attempt to recognize the requested listing and locality and search a special database containing a predefined listing of the most frequently requested numbers. This database that is separate from the primary directory assist database has proven to be very costly to initially create and to subsequently maintain. In essence, statistical data is gathered from the primary directory assistance database to determine the most frequently requested listings. Once the most frequently requested listings are identified, each listing is associated with a particular locality and number. The rigidity of this association precludes efficient updating, and more importantly, prevents intermingling frequently requested listings and localities for voice recognition. Further, the automation can only occur for previously defined listings incorporated into the special directory assistance database. Less than five percent of calls are automated in the few systems in operation.
Large department stores, such as Kmart or Wal-Mart are typically some of the most frequently requested listings and highlight the deficiencies of the current systems. Assume that the most frequently requested listings are gathered for North Carolina and that there are approximately 100 Kmarts in North Carolina. Statistically, the most frequently requested Kmart listings are those in the more metropolitan areas. As such, although there are 100 Kmarts in North Carolina, only 10 may be within the top 2000 most frequently requested listings. The remaining Kmart listings are not listed in the special directory assistance database and will not be automated, even though the speech recognition system could theoretically recognize the name Kmart and likely synonyms, such as Super Kmart or Big K. Automation is prohibited for new Kmarts until the special directory assistance database is updated and the listing makes its way into the top 2000 most frequently requested listings. Moreover, frequently requested Kmarts that close, move or change numbers are not considered until the special directory assistance database is updated.
These limitations to directory assistance automation continue to render commercial embodiments impractical. Thus, there is a need for a more efficient directory assistance automation overcoming the deficiencies of existing systems.